https://github.com/zeroto521/kmeans

A Python implementation of base KMEANS, SEED-KMEANS algorithm
https://github.com/zeroto521/kmeans

Last synced: about 2 months ago
JSON representation

A Python implementation of base KMEANS, SEED-KMEANS algorithm

• Host: GitHub
• URL: https://github.com/zeroto521/kmeans
• Owner: Zeroto521
• License: mit
• Created: 2019-05-12T07:21:07.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2020-06-12T02:01:45.000Z (almost 5 years ago)
• Last Synced: 2025-02-09T01:33:16.511Z (4 months ago)
• Language: Python
• Homepage:
• Size: 15.6 KB
• Stars: 1
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# KMeans

A Python implementation of base KMEANS, SEED-KMEANS algorithm.

[![Build Status](https://travis-ci.com/Zeroto521/KMeans.svg?branch=master)](https://travis-ci.com/Zeroto521/KMeans) [![codecov](https://codecov.io/gh/Zeroto521/kmeans/branch/master/graph/badge.svg)](https://codecov.io/gh/Zeroto521/kmeans)

## Prerequisites

-   numpy

> More details for [requirements](requirements.txt) file.

## Installation

```bash

>>> git clone https://github.com/Zeroto521/KMeans.git

>>> cd KMeans

>>> python setup.py install

```

## Examples

```bash

>>> from kmeans import KMeans

>>> model = KMeans()  # build model

>>> labels = model.fit(data)  # Guess you have `data` which the shape is `(n, m)`. `n` is sample numbers, `m` is feature numbers.

>>> labels

```

> more examples can see [here](./examples).

## License

MIT License. [@Zeroto521](https://github.com/Zeroto521)

## TODO

-   [ ] all kinds of distane

-   [ ] write doc

-   [ ] Optimized sparse matrix